Surfactant flow and transport in the vadose zone: a numerical experiment
Bibliographic record
Abstract
The presence of surfactants in the unsaturated zone is one of the scenarios where the flow depends on the solute concentration. The simulation of flow and transport of surfactants requires the coupling of flow and transport, where the surfactant concentration-dependent changes to water retention (i.e. changes in surface tension and/or contact angle) and hydraulic conductivity need to be considered. This research presents a numerical experiment that compares surfactant flow and transport under intermittent boundary conditions to that of a conservative tracer. A modified version of Hydrus 2D, with the concentration-dependent surface tension and viscosity effects, is used. The numerical experiment examines the effect of surfactant infiltration, redistribution and flushing in a low-water content unsaturated porous medium with a deep water table. The numerical experiment encompasses not only the hysteresis as a result of transient localised drainage and rewetting associated with the surfactant-induced pressure gradients, and the intermittent boundary conditions. The comparison of surfactant flow to that of a conservative tracer indicates significant differences for the same boundary conditions. The results from this experiment will be beneficial for the design and optimisation of infiltration galleries employing surfactants for soil and ground water remediation, as well as for the understanding the flow and transport of grey water in the unsaturated zone.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".